Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Expressions.

Overview

patterns-finder

Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Expressions.

This library offers the capabilities:

  • A set of predefined patterns with the most useful regex.
  • Extend the patterns, by adding user defined regex.
  • Find and extarct patterns from text
  • Pandas' Dataframe support.
  • Sort the results of extraction.
  • Summarize the results of extraction.
  • Display extractions by visualy rich text annotation.
  • Build complex extraction rules based on regex (in future release).

Installation

To install the last version of patterns-finder library, use pip:

pip install patterns-finder

Usage

Find a pattern in the text

Just import patterns, like emoji from patterns_finder.patterns.web, then you can use them to find pattern in text:

from patterns_finder.patterns.web import emoji, url, email 

emoji.find("the quick #A52A2A 🦊 jumped 3 times over the lazy 🐶 ")
# Output:
# [(18, 19, 'EMOJI', '🦊'), (49, 50, 'EMOJI', '🐶')]

url.find("The lazy 🐶 has a website https://lazy.dog.com ")
# Output:
# [(25, 45, 'URL', 'https://lazy.dog.com')]

email.find("[email protected] is the email of 🦊 ")
# Output:
# [(0, 19, 'EMAIL', '[email protected]')]

The results provided by the method find for each of pattern are in the form:

[(0, 19, 'EMAIL', '[email protected]')]
  ^  ^       ^          ^ 
  |  |       |          |
 Offset      |          └ Text matching the pattern
  |  |       └ Label of the pattern
  |  └ End index
  └ Start index in the text

Find multiple patterns in the text

To search for different patterns in the text we can use the method finder.patterns_in_text(text, patterns) as follows:

from patterns_finder import finder
from patterns_finder.patterns.web import emoji, url, color_hex
from patterns_finder.patterns.number import integer

patterns = [emoji, color_hex, integer]
text = "the quick #A52A2A 🦊 jumped 3 times over the lazy 🐶 "
finder.patterns_in_text(text, patterns)
# Output:
# [(18, 19, 'EMOJI', '🦊'),
#  (49, 50, 'EMOJI', '🐶'),
#  (10, 17, 'COLOR_HEX', '#A52A2A'),
#  (12, 14, 'INTEGER', '52'),
#  (15, 16, 'INTEGER', '2'),
#  (27, 28, 'INTEGER', '3')]

Find user defined patterns in the text

To define new pattern you can use any regex pattern that are supported by the regex and re packages of python. User defined patterns can be writen in the form of string regex pattern or tuple of string ('regex pattern', 'label').

patterns = [web.emoji, "quick|lazy", ("\\b[a-zA-Z]+\\b", "WORD") ]
text = "the quick #A52A2A 🦊 jumped 3 times over the lazy 🐶 "
finder.patterns_in_text(text, patterns)
# Output: 
# [(18, 19, 'EMOJI', '🦊'),
#  (49, 50, 'EMOJI', '🐶'),
#  (4, 9, 'quick|lazy', 'quick'),
#  (44, 48, 'quick|lazy', 'lazy'),
#  (0, 3, 'WORD', 'the'),
#  (4, 9, 'WORD', 'quick'),
#  (20, 26, 'WORD', 'jumped'),
#  (29, 34, 'WORD', 'times'),
#  (35, 39, 'WORD', 'over'),
#  (40, 43, 'WORD', 'the'),
#  (44, 48, 'WORD', 'lazy')]

Sort extraxted patterns

By using the argument sort_by of the method finder.patterns_in_text we can sort the extraction accoring to different options:

  • sort_by=finder.START sorts the results by the start index in the text
patterns = [web.emoji, color_hex, ('\\b[a-zA-Z]+\\b', 'WORD') ]
finder.patterns_in_text(text, patterns, sort_by=finder.START)
# Output:
# [(0, 3, 'WORD', 'the'),
#  (4, 9, 'WORD', 'quick'),
#  (10, 17, 'COLOR_HEX', '#A52A2A'),
#  (18, 19, 'EMOJI', '🦊'),
#  (20, 26, 'WORD', 'jumped'),
#  (29, 34, 'WORD', 'times'),
#  (35, 39, 'WORD', 'over'),
#  (40, 43, 'WORD', 'the'),
#  (44, 48, 'WORD', 'lazy'),
#  (49, 50, 'EMOJI', '🐶')]
  • sort_by=finder.END sorts the results by the end index in the text
finder.patterns_in_text(text, patterns, sort_by=finder.END)
# Output:
# [(0, 3, 'WORD', 'the'),
#  (4, 9, 'WORD', 'quick'),
#  (10, 17, 'COLOR_HEX', '#A52A2A'),
#  (18, 19, 'EMOJI', '🦊'),
#  (20, 26, 'WORD', 'jumped'),
#  (29, 34, 'WORD', 'times'),
#  (35, 39, 'WORD', 'over'),
#  (40, 43, 'WORD', 'the'),
#  (44, 48, 'WORD', 'lazy'),
#  (49, 50, 'EMOJI', '🐶')]
  • sort_by=finder.LABEL sorts the results by pattern's label
finder.patterns_in_text(text, patterns, sort_by=finder.LABEL)
# Output:
# [(10, 17, 'COLOR_HEX', '#A52A2A'),
#  (18, 19, 'EMOJI', '🦊'),
#  (49, 50, 'EMOJI', '🐶'),
#  (0, 3, 'WORD', 'the'),
#  (4, 9, 'WORD', 'quick'),
#  (20, 26, 'WORD', 'jumped'),
#  (29, 34, 'WORD', 'times'),
#  (35, 39, 'WORD', 'over'),
#  (40, 43, 'WORD', 'the'),
#  (44, 48, 'WORD', 'lazy')]
  • sort_by=finder.TEXT sorts the results by the extracted text
finder.patterns_in_text(text, patterns, sort_by=finder.TEXT)
# Output:
# [(10, 17, 'COLOR_HEX', '#A52A2A'),
#  (20, 26, 'WORD', 'jumped'),
#  (44, 48, 'WORD', 'lazy'),
#  (35, 39, 'WORD', 'over'),
#  (4, 9, 'WORD', 'quick'),
#  (0, 3, 'WORD', 'the'),
#  (40, 43, 'WORD', 'the'),
#  (29, 34, 'WORD', 'times'),
#  (49, 50, 'EMOJI', '🐶'),
#  (18, 19, 'EMOJI', '🦊')]

Summarize results of extraction

By using the argument summary_type, one can choose the desired form of output results.

  • summary_type=finder.NONE retruns a list with all details, without summarization.
patterns = [ color_hex, ('\\b[a-zA-Z]+\\b', 'WORD'), web.emoji ]
finder.patterns_in_text(text, patterns, summary_type=finder.NONE)
# Output:
# [(10, 17, 'COLOR_HEX', '#A52A2A'),
#  (0, 3, 'WORD', 'the'),
#  (4, 9, 'WORD', 'quick'),
#  (20, 26, 'WORD', 'jumped'),
#  (29, 34, 'WORD', 'times'),
#  (35, 39, 'WORD', 'over'),
#  (40, 43, 'WORD', 'the'),
#  (44, 48, 'WORD', 'lazy'),
#  (18, 19, 'EMOJI', '🦊'),
#  (49, 50, 'EMOJI', '🐶')]
  • summary_type=finder.LABEL_TEXT_OFFSET returns a dictionary of patterns labels as keys, with the corresponding offsets and text as values.
finder.patterns_in_text(text, patterns, summary_type=finder.LABEL_TEXT_OFFSET)
# Output:
# {
#  'COLOR_HEX': [[10, 17, '#A52A2A']],
#  'WORD': [[0, 3, 'the'], [4, 9, 'quick'], [20, 26, 'jumped'], [29, 34, 'times'], [35, 39, 'over'], [40, 43, 'the'], [44, 48, 'lazy']],
#  'EMOJI': [[18, 19, '🦊'], [49, 50, '🐶']]
# }
  • summary_type=finder.LABEL_TEXT returns a dictionary of patterns labels as keys, with the corresponding text (without offset) as values.
finder.patterns_in_text(text, patterns, summary_type=finder.LABEL_TEXT)
# Output:
# {
#  'COLOR_HEX': ['#A52A2A'],
#  'WORD': ['the', 'quick', 'jumped', 'times', 'over', 'the', 'lazy'],
#  'EMOJI': ['🦊', '🐶']
# }
  • summary_type=finder.TEXT_ONLY returns a list of the extracted text only.
finder.patterns_in_text(text, patterns, summary_type=finder.TEXT_ONLY)
# Output:
# ['#A52A2A', 'the', 'quick', 'jumped', 'times', 'over', 'the', 'lazy', '🦊', '🐶']

Extract patterns from Pandas DataFrame

This package provides the capability to extract patterns from Pandas' DataFrame easily, by using the method finder.patterns_in_df(df, input_col, output_col, patterns, ...).

from patterns_finder import finder
from patterns_finder.patterns import web
import pandas as pd

patterns = [web.email, web.emoji, web.url]

df = pd.DataFrame(data={
    'text': ["the quick #A52A2A 🦊 jumped 3 times over the lazy 🐶",
                    "[email protected] is the email of 🦊",
                    "The lazy 🐶 has a website https://lazy.dog.com"],
    })

finder.patterns_in_df(df, "text", "extraction", patterns, summary_type=finder.LABEL_TEXT)
# Output:
# |    | text                                                 | extraction                                          |
# |---:|:-----------------------------------------------------|:----------------------------------------------------|
# |  0 | the quick #A52A2A 🦊 jumped 3 times over the lazy 🐶 | {'EMOJI': ['🦊', '🐶']}                            |
# |  1 | [email protected] is the email of 🦊               | {'EMAIL': ['[email protected]'], 'EMOJI': ['🦊']} |
# |  2 | The lazy 🐶 has a website https://lazy.dog.com       | {'EMOJI': ['🐶'], 'URL': ['https://lazy.dog.com']}  |

The method finder.patterns_in_df have also the arguments summary_type and sort_by.

List of all predefined patterns

  • Web
from patterns_finder.web import email, url, uri, mailto, html_link, sql, color_hex, copyright, alphanumeric, emoji, username, quotation, ipv4, ipv6
  • Phone
from patterns_finder.phone import generic, uk, us
  • Credit Cards
from patterns_finder.credit_card import generic, visa, mastercard, discover, american_express
  • Numbers
from patterns_finder.number import integer, float, scientific, hexadecimal, percent, roman
  • Currency
from patterns_finder.currency import monetary, symbol, code, name
  • Languages
from patterns_finder.language import english, french, spanish, arabic, hebrew, turkish, russian, german, chinese, greek, japanese, hindi, bangali, armenian, swedish, portoguese, balinese, georgian
  • Time and Date
from patterns_finder.time_date import time, date, year
  • Postal Code
from patterns_finder.postal_code import us, canada, uk, france, spain, switzerland, brazilian

Contact

Please email your questions or comments to me.

You might also like...
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr

texlive expressions for documents

tex2nix Generate Texlive environment containing all dependencies for your document rather than downloading gigabytes of texlive packages. Installation

DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task

DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task。涵盖68个领域、共计916万词的专业词典知识库,可用于文本分类、知识增强、领域词汇库扩充等自然语言处理应用。

This repository contains Python scripts for extracting linguistic features from Filipino texts.

Filipino Text Linguistic Feature Extractors This repository contains scripts for extracting linguistic features from Filipino texts. The scripts were

Extracting Summary Knowledge Graphs from Long Documents

GraphSum This repo contains the data and code for the G2G model in the paper: Extracting Summary Knowledge Graphs from Long Documents. The other basel

Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.
PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

Modular and extensible speech recognition library leveraging pytorch-lightning and hydra.

Lightning ASR Modular and extensible speech recognition library leveraging pytorch-lightning and hydra What is Lightning ASR • Installation • Get Star

Comments
  • Add Support for Patents patterns

    Add Support for Patents patterns

    Support Patent patterns w/ first implementation to support Patents globally

    Example usage:

    from patterns_finder.patterns.patents import global_patent
    global_patent.find("Patent US5960368A is titled Method for acid oxidation of radioactive, hazardous, and mixed organic waste materials ")
    # Output:
    # [(7, 16, 'PATENT', 'US5960368A')]
    
    

    requesting permission to add the patterns :p

    opened by mahzy 0
Releases(1.0.1)
Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

9 Jan 08, 2023
多语言降噪预训练模型MBart的中文生成任务

mbart-chinese 基于mbart-large-cc25 的中文生成任务 Input source input: text + /s + lang_code target input: lang_code + text + /s Usage token_ids_mapping.jso

11 Sep 19, 2022
Speech Recognition for Uyghur using Speech transformer

Speech Recognition for Uyghur using Speech transformer Training: this model using CTC loss and Cross Entropy loss for training. Download pretrained mo

Uyghur 11 Nov 17, 2022
Chinese NER with albert/electra or other bert descendable model (keras)

Chinese NLP (albert/electra with Keras) Named Entity Recognization Project Structure ./ ├── NER │   ├── __init__.py │   ├── log

2 Nov 20, 2022
An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)

VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation

Facebook Research 409 Oct 28, 2022
Awesome Treasure of Transformers Models Collection

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Ashish Patel 577 Jan 07, 2023
BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network) BERTAC is a framework that combines a

6 Jan 24, 2022
Shirt Bot is a discord bot which uses GPT-3 to generate text

SHIRT BOT · Shirt Bot is a discord bot which uses GPT-3 to generate text. Made by Cyclcrclicly#3420 (474183744685604865) on Discord. Support Server EX

31 Oct 31, 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
Maha is a text processing library specially developed to deal with Arabic text.

An Arabic text processing library intended for use in NLP applications Maha is a text processing library specially developed to deal with Arabic text.

Mohammad Al-Fetyani 184 Nov 27, 2022
Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology (EARIST)

🤖 Coeus - EARIST A.C.E 💬 Coeus is an Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology,

Dids Irwyn Reyes 3 Oct 14, 2022
UniSpeech - Large Scale Self-Supervised Learning for Speech

UniSpeech The family of UniSpeech: WavLM (arXiv): WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing UniSpeech (ICML 202

Microsoft 281 Dec 15, 2022
2021 2학기 데이터크롤링 기말프로젝트

공지 주제 웹 크롤링을 이용한 취업 공고 스케줄러 스케줄 주제 정하기 코딩하기 핵심 코드 설명 + 피피티 구조 구상 // 12/4 토 피피티 + 스크립트(대본) 제작 + 녹화 // ~ 12/10 ~ 12/11 금~토 영상 편집 // ~12/11 토 웹크롤러 사람인_평균

Choi Eun Jeong 2 Aug 16, 2022
Pipelines de datos, 2021.

Este repo ilustra un proceso sencillo de automatización de transformación y modelado de datos, a través de un pipeline utilizando Luigi. Stack princip

Rodolfo Ferro 8 May 19, 2022
Weakly-supervised Text Classification Based on Keyword Graph

Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C

Hello_World 20 Dec 29, 2022
Trains an OpenNMT PyTorch model and SentencePiece tokenizer.

Trains an OpenNMT PyTorch model and SentencePiece tokenizer. Designed for use with Argos Translate and LibreTranslate.

Argos Open Tech 61 Dec 13, 2022
Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Official code for our Interspeech 2021 - Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset [1]*. Visually-grounded spoken language datasets c

Ian Palmer 3 Jan 26, 2022
**NSFW** A chatbot based on GPT2-chitchat

DangBot -- 好怪哦,再来一句 卡群怪话bot,powered by GPT2 for Chinese chitchat Training Example: python train.py --lr 5e-2 --epochs 30 --max_len 300 --batch_size 8

Tommy Yang 11 Jul 21, 2022
NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT

NeuralQA: A Usable Library for (Extractive) Question Answering on Large Datasets with BERT Still in alpha, lots of changes anticipated. View demo on n

Victor Dibia 220 Dec 11, 2022
Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Ubiquitous Knowledge Processing Lab 59 Dec 01, 2022